OpenRaiser/Intern-Atlas huggingface dataset:
Solving the Buyer and Seller's Dilemma: A Dual-Deposit Escrow Smart Contract for Provably Cheat-Proof Delivery and Payment for a Digital Good without a Trusted Mediator
Venue: arXiv cs.CR 2018
Year: 2018

A fundamental problem for electronic commerce is the buying and selling of digital goods between individuals that may not know or trust each other. Traditionally, this problem has been addressed by the use of trusted third-parties such as credit-card companies, mediated escrows, legal adjudication, or reputation systems. Despite the rise of blockchain protocols as a way to send payments without trusted third parties, the important problem of exchanging a digital good for payment without trusted third parties has been paid much less attention. We refer to this problem as the Buyer and Seller's Dilemma and present for it a dual-deposit escrow trade protocol which uses double-sided payment deposits in conjunction with simple cryptographic primitives, and that can be implemented using a blockchain-based smart contract. We analyze our protocol as an extensive-form game and prove that the Sub-game Perfect Nash Equilibrium for this game is for both the buyer and seller to cooperate and behave honestly. We address this problem under the assumption that the digital good being traded is known and verifiable, with a fixed price known to both parties.
Automated Detection of Serializability Violations under Weak Consistency
Venue: arXiv cs.PL 2018
Year: 2018

While a number of weak consistency mechanisms have been developed in recent years to improve performance and ensure availability in distributed, replicated systems, ensuring correctness of transactional applications running on top of such systems remains a difficult and important problem. Serializability is a well-understood correctness criterion for transactional programs; understanding whether applications are serializable when executed in a weakly-consistent environment, however remains a challenging exercise. In this work, we combine the dependency graph-based characterization of serializability and the framework of abstract executions to develop a fully automated approach for statically finding bounded serializability violations under \emph{any} weak consistency model. We reduce the problem of serializability to satisfiability of a formula in First-Order Logic, which allows us to harness the power of existing SMT solvers. We provide rules to automatically construct the FOL encoding from programs written in SQL (allowing loops and conditionals) and the consistency specification written as a formula in FOL. In addition to detecting bounded serializability violations, we also provide two orthogonal schemes to reason about unbounded executions by providing sufficient conditions (in the form of FOL formulae) whose satisfiability would imply the absence of anomalies in any arbitrary execution. We have applied the proposed technique on TPC-C, a real world database program with complex application logic, and were able to discover anomalies under Parallel Snapshot Isolation, and verify serializability for unbounded executions under Snapshot Isolation, two consistency mechanisms substantially weaker than serializability.
A General Approach to State Complexity of Operations: Formalization and Limitations
Venue: arXiv cs.FL 2018
Year: 2018

The state complexity of the result of a regular operation is often positively correlated with the number of distinct transformations induced by letters in the minimal deterministic finite automaton of the input languages. That is, more transformations in the inputs means higher state complexity in the output. When this correlation holds, the state complexity of a unary operation can be maximized using languages in which there is one letter corresponding to each possible transformation; for operations of higher arity, we can use $m$-tuples of languages in which there is one letter corresponding to each possible $m$-tuple of transformations. In this way, a small set of languages can be used as witnesses for many common regular operations, eliminating the need to search for witnesses -- though at the expense of using very large alphabets. We formalize this approach and examine its limitations. We define a class of "uniform" operations for which this approach works; the class is closed under composition and includes common operations such as star, concatenation, reversal, union, and complement. Our main result is that the worst-case state complexity of a uniform operation can be determined by considering a finite set of witnesses, and this set depends only on the arity of the operation and the state complexities of the inputs.
Deep Drone Racing: Learning Agile Flight in Dynamic Environments
Venue: arXiv cs.RO 2018
Year: 2018

Autonomous agile flight brings up fundamental challenges in robotics, such as coping with unreliable state estimation, reacting optimally to dynamically changing environments, and coupling perception and action in real time under severe resource constraints. In this paper, we consider these challenges in the context of autonomous, vision-based drone racing in dynamic environments. Our approach combines a convolutional neural network (CNN) with a state-of-the-art path-planning and control system. The CNN directly maps raw images into a robust representation in the form of a waypoint and desired speed. This information is then used by the planner to generate a short, minimum-jerk trajectory segment and corresponding motor commands to reach the desired goal. We demonstrate our method in autonomous agile flight scenarios, in which a vision-based quadrotor traverses drone-racing tracks with possibly moving gates. Our method does not require any explicit map of the environment and runs fully onboard. We extensively test the precision and robustness of the approach in simulation and in the physical world. We also evaluate our method against state-of-the-art navigation approaches and professional human drone pilots.
The Temporal Singularity: time-accelerated simulated civilizations and their implications
Venue: arXiv cs.AI 2018
Year: 2018

Provided significant future progress in artificial intelligence and computing, it may ultimately be possible to create multiple Artificial General Intelligences (AGIs), and possibly entire societies living within simulated environments. In that case, it should be possible to improve the problem solving capabilities of the system by increasing the speed of the simulation. If a minimal simulation with sufficient capabilities is created, it might manage to increase its own speed by accelerating progress in science and technology, in a way similar to the Technological Singularity. This may ultimately lead to large simulated civilizations unfolding at extreme temporal speedups, achieving what from the outside would look like a Temporal Singularity. Here we discuss the feasibility of the minimal simulation and the potential advantages, dangers, and connection to the Fermi paradox of the Temporal Singularity. The medium-term importance of the topic derives from the amount of computational power required to start the process, which could be available within the next decades, making the Temporal Singularity theoretically possible before the end of the century.
Deep Spectral Convolution Network for HyperSpectral Unmixing
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures with a deeper network architecture. Second, instead of batch normalization, we propose a spectral normalization layer which improves the selectivity of filters by normalizing their spectral responses. Third, we introduce two fusion configurations that produce ideal abundance maps by using the abstract representations computed from previous layers. In experiments, we use two real datasets to evaluate the performance of our method with other baseline techniques. The experimental results validate that the proposed method outperforms baselines based on Root Mean Square Error (RMSE).
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results.
Deploying Deep Neural Networks in the Embedded Space
Venue: arXiv cs.CV 2018
Year: 2018

Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of intelligent applications. This paper summarises our recent work on the optimised mapping of DNNs on embedded settings. By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the deployment of sophisticated deep learning models on cutting-edge mobile and embedded systems.
Matrix Completion and Performance Guarantees for Single Individual Haplotyping
Venue: arXiv cs.LG 2018
Year: 2018

Single individual haplotyping is an NP-hard problem that emerges when attempting to reconstruct an organism's inherited genetic variations using data typically generated by high-throughput DNA sequencing platforms. Genomes of diploid organisms, including humans, are organized into homologous pairs of chromosomes that differ from each other in a relatively small number of variant positions. Haplotypes are ordered sequences of the nucleotides in the variant positions of the chromosomes in a homologous pair; for diploids, haplotypes associated with a pair of chromosomes may be conveniently represented by means of complementary binary sequences. In this paper, we consider a binary matrix factorization formulation of the single individual haplotyping problem and efficiently solve it by means of alternating minimization. We analyze the convergence properties of the alternating minimization algorithm and establish theoretical bounds for the achievable haplotype reconstruction error. The proposed technique is shown to outperform existing methods when applied to synthetic as well as real-world Fosmid-based HapMap NA12878 datasets.
Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning
Venue: arXiv cs.CV 2018
Year: 2018

Interventional C-arm imaging is crucial to percutaneous orthopedic procedures as it enables the surgeon to monitor the progress of surgery on the anatomy level. Minimally invasive interventions require repeated acquisition of X-ray images from different anatomical views to verify tool placement. Achieving and reproducing these views often comes at the cost of increased surgical time and radiation dose to both patient and staff. This work proposes a marker-free "technician-in-the-loop" Augmented Reality (AR) solution for C-arm repositioning. The X-ray technician operating the C-arm interventionally is equipped with a head-mounted display capable of recording desired C-arm poses in 3D via an integrated infrared sensor. For C-arm repositioning to a particular target view, the recorded C-arm pose is restored as a virtual object and visualized in an AR environment, serving as a perceptual reference for the technician. We conduct experiments in a setting simulating orthopedic trauma surgery. Our proof-of-principle findings indicate that the proposed system can decrease the 2.76 X-ray images required per desired view down to zero, suggesting substantial reductions of radiation dose during C-arm repositioning. The proposed AR solution is a first step towards facilitating communication between the surgeon and the surgical staff, improving the quality of surgical image acquisition, and enabling context-aware guidance for surgery rooms of the future. The concept of technician-in-the-loop design will become relevant to various interventions considering the expected advancements of sensing and wearable computing in the near future.
Staircase-PIR: Universally Robust Private Information Retrieval
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of designing private information retrieval (PIR) schemes on data of $m$ files replicated on $n$ servers that can possibly collude. We focus on devising robust PIR schemes that can tolerate stragglers, i.e., slow or unresponsive servers. In many settings, the number of stragglers is not known a priori or may change with time. We define universally robust PIR as schemes that achieve PIR capacity asymptotically in $m$ and simultaneously for any number of stragglers up to a given threshold. We introduce Staircase-PIR schemes and prove that they are universally robust. Towards that end, we establish an equivalence between robust PIR and communication efficient secret sharing.
A Nearly-Linear Bound for Chasing Nested Convex Bodies
Venue: arXiv cs.DS 2018
Year: 2018

Friedman and Linial introduced the convex body chasing problem to explore the interplay between geometry and competitive ratio in metrical task systems. In convex body chasing, at each time step $t \in \mathbb{N}$, the online algorithm receives a request in the form of a convex body $K_t \subseteq \mathbb{R}^d$ and must output a point $x_t \in K_t$. The goal is to minimize the total movement between consecutive output points, where the distance is measured in some given norm. This problem is still far from being understood, and recently Bansal et al. gave an algorithm for the nested version, where each convex body is contained within the previous one. We propose a different strategy which is $O(d \log d)$-competitive algorithm for this nested convex body chasing problem, improving substantially over previous work. Our algorithm works for any norm. This result is almost tight, given an $Ω(d)$ lower bound for the $\ell_{\infty}$.
An Inductive Formalization of Self Reproduction in Dynamical Hierarchies
Venue: arXiv cs.AI 2018
Year: 2018

Formalizing self reproduction in dynamical hierarchies is one of the important problems in Artificial Life (AL) studies. We study, in this paper, an inductively defined algebraic framework for self reproduction on macroscopic organizational levels under dynamical system setting for simulated AL models and explore some existential results. Starting with defining self reproduction for atomic entities we define self reproduction with possible mutations on higher organizational levels in terms of hierarchical sets and the corresponding inductively defined `meta' - reactions. We introduce constraints to distinguish a collection of entities from genuine cases of emergent organizational structures.
Retweet Us, We Will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services
Venue: arXiv cs.SI 2018
Year: 2018

Twitter has increasingly become a popular platform to share news and user opinion. A tweet is considered to be important if it receives high number of affirmative reactions from other Twitter users via Retweets. Retweet count is thus considered as a surrogate measure for positive crowd-sourced reactions - high number of retweets of a tweet aid in making its topic trending. This in turn bolsters the social reputation of the author of the tweet. Since social reputation/impact of users/tweets influences many decisions (such as promoting brands, advertisement etc.), several blackmarket syndicates have actively been engaged in producing fake retweets in a collusive manner. Users who want to boost the impact of their tweets approach the blackmarket services, and gain retweets for their own tweets by either paying money (Premium Services) or by retweeting other customers' tweets. Thus they become customers of blackmarket syndicates and engage in fake activities. Interestingly, these customers are neither bots, nor even fake users - they are usually normal human beings; they express a mix of organic and inorganic retweeting activities, and there is no synchronicity across their behaviors. In this paper, we make a first attempt to investigate such blackmarket customers engaged in producing fake retweets. We collected and annotated a novel dataset comprising of customers of many blackmarket services and show how their social behavior differs from genuine users. We then use state-of-the-art supervised models to detect three types of customers (bots, promotional, normal) and genuine users. We achieve a Macro F1-score of 0.87 with SVM, outperforming four other baselines significantly. We further design a browser extension, SCoRe which, given the link of a tweet, spots its fake retweeters in real-time. We also collected users' feedback on the performance of SCoRe and obtained 85% accuracy.
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech
Venue: arXiv cs.SD 2018
Year: 2018

Current approaches to speech emotion recognition focus on speech features that can capture the emotional content of a speech signal. Mel Frequency Cepstral Coefficients (MFCCs) are one of the most commonly used representations for audio speech recognition and classification. This paper proposes Gammatone Frequency Cepstral Coefficients (GFCCs) as a potentially better representation of speech signals for emotion recognition. The effectiveness of MFCC and GFCC representations are compared and evaluated over emotion and intensity classification tasks with fully connected and recurrent neural network architectures. The results provide evidence that GFCCs outperform MFCCs in speech emotion recognition.
Sum-Rate Performance of Millimeter Wave MIMO Shared Spectrum Systems
Venue: arXiv cs.IT 2018
Year: 2018

This paper study the effect of interference management arising due to millimeter wave (mmWave) communication on the performance of multiple input multiple output (MIMO) based spectrum sharing cognitive systems. The highly directed mmWave signals have short wavelength property which enables to fabricate an increased number of antennas in a small confined space of the communication system. The large antenna elements are utilized to harvest high beamforming gains and to improve the signal to noise ratio (SNR) by analog processing at the cognitive base station (CBS) and secondary users. Analog processing at the CBS and at secondary user have hardware and computation restrictions. These restrictions are solved by designing radio frequency (RF) precoding and RF combining. In the digital domain, it is shown that interference between secondary users can be canceled by baseband block diagonalization technique. Towards this end, the underlay mode of operation is considered to control interference on the primary user and to derive the expression for optimal power allocation. The derived power allocation is a solution to the rate maximization problem of mmWave MIMO cognitive radio (CR) systems. Finally, numerical simulations are performed to yield various significant insights into for different mmWave CR system conditions and also validate the improvement in performance in comparison to the only digital communication systems.
Considerations for a PAP Smear Image Analysis System with CNN Features
Venue: arXiv cs.CV 2018
Year: 2018

It has been shown that for automated PAP-smear image classification, nucleus features can be very informative. Therefore, the primary step for automated screening can be cell-nuclei detection followed by segmentation of nuclei in the resulting single cell PAP-smear images. We propose a patch based approach using CNN for segmentation of nuclei in single cell images. We then pose the question of ion of segmentation for classification using representation learning with CNN, and whether low-level CNN features may be useful for classification. We suggest a CNN-based feature level analysis and a transfer learning based approach for classification using both segmented as well full single cell images. We also propose a decision-tree based approach for classification. Experimental results demonstrate the effectiveness of the proposed algorithms individually (with low-level CNN features), and simultaneously proving the sufficiency of cell-nuclei detection (rather than accurate segmentation) for classification. Thus, we propose a system for analysis of multi-cell PAP-smear images consisting of a simple nuclei detection algorithm followed by classification using transfer learning.
Disease Classification in Metagenomics with 2D Embeddings and Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning (DL) techniques have shown unprecedented success when applied to images, waveforms, and text. Generally, when the sample size ($N$) is much bigger than the number of features ($d$), DL often outperforms other machine learning (ML) techniques, often through the use of Convolutional Neural Networks (CNNs). However, in many bioinformatics fields (including metagenomics), we encounter the opposite situation where $d$ is significantly greater than $N$. In these situations, applying DL techniques would lead to severe overfitting. Here we aim to improve classification of various diseases with metagenomic data through the use of CNNs. For this we proposed to represent metagenomic data as images. The proposed Met2Img approach relies on taxonomic and t-SNE embeddings to transform abundance data into "synthetic images". We applied our approach to twelve benchmark data sets including more than 1400 metagenomic samples. Our results show significant improvements over the state-of-the-art algorithms (Random Forest (RF), Support Vector Machine (SVM)). We observe that the integration of phylogenetic information alongside abundance data improves classification. The proposed approach is not only important in classification setting but also allows to visualize complex metagenomic data. The Met2Img is implemented in Python.
Generative Models for Pose Transfer
Venue: arXiv cs.GR 2018
Year: 2018

We investigate nearest neighbor and generative models for transferring pose between persons. We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions. Our generative model (pix2pix) outperforms k-NN at both generating corresponding frames and generalizing outside the demonstrated action set. Our most salient contribution is determining a pipeline (pose detection, face detection, k-NN based pairing) that is effective at perform-ing the desired task. We also detail several iterative improvements and failure modes.